Advanced Data Mining and Applications

Deep Learning .- TeaE: an Efficient Method for Improving the Precision of Teaching Evaluation.- Graph Fusion Multimodal Named Entity Recognition Based on Auxiliary Relation Enhancement.- Sentence-level Event Detection without Triggers via Prompt Learning and Machine Reading Comprehension.- Multi-grained Logical Graph Network for Reasoning-based Machine Reading Comprehension.- Adaptive Prototype Learning with Common and Discriminative Features for Few-shot Relation Extraction.- Fine-grained Knowledge Enhancement for Empathetic Dialogue Generation.- Implicit Sentiment Extraction using Structure Generation with Sentiment Instructor Prompt Template.- SE-Prompt: Exploring Semantic Enhancement with Prompt Tuning for Relation Extraction.- Self-supervised Multi-view Clustering Framework with Graph Filtering and Contrast Fusion.- Semantic Selection and Multi-view Alignment for Image-Text Retrieval.- Voice Conversion with Denoising Diffusion Probabilistic GAN Models.- Symbolic & Acoustic: Multi-domain Music Emotion Modeling for Instrumental Music.- Document-level Relation Extraction with Relational Reasoning and Heterogeneous Graph Neural Networks.- A Chinese Named Entity Recognition Method based on Textual Information Perception Fusion.- Aspect-Based Sentiment Analysis via BERT and Multi-Scale CBAM.- A novel adaptive distribution distance-based feature selection method for video traffic identification.- SVIM: a Skeleton-based View-invariant Method for Online Gesture Recognition.- A Unified Information Diffusion Prediction Model based on Multi-task Learning.- Learning Knowledge Representation with Entity Concept Information.- Domain Adaptive Pre-trained Model for Mushroom Image Classification.- Training Noise Robust Deep Neural Networks with Self-supervised Learning.- Path integration enhanced graph attention network.- Graph Contrastive Learning with HybridNoise Augmentation for Recommendation.- User-Oriented Interest Representation on Knowledge Graph for Long-Tail Recommendation.- Multi-Self-Supervised Light Graph Convolution Network for Social Recommendation.- A Poisoning Attack Based on Variant Generative Adversarial Networks in Recommender Systems.- Label Correlation guided Feature Selection for Multi-label Learning.- Iterative Encode-and-Decode Graph Neural Network.- Community Detection in Temporal Biological Metabolic Networks based on Semi-NMF Method with Node Similarity Fusion.- UKGAT: Uncertain Knowledge Graph Embedding Enriched KGAT for Recommendation.- Knowledge Graph Link Prediction Model Based on Attention Graph Convolutional Network.- Knowledge Graph Embedding with Relation Rotation and Entity Adjustment by Quaternions.- Towards time-variant-aware Link Prediction in Dynamic Graph through Self-supervised Learning.- Adaptive Heterogeneous graph Contrastive clustering with Multi-Similarity.- Multi-Teacher Local Semantic Distillation from Graph Neural Networks.- AutoAM: An End-To-End Neural Model for Automatic and Universal Argument Mining.- Rethinking the Evaluation of Deep Neural Network Robustness.- A Visual Interpretation-Based Self-Improved Classification System Using Virtual Adversarial Training.- TSCMR:Two-Stage Cross-Modal Retrieval.- Effi-Emp: An AI based approach towards positive empathic expressions.- Industry Track Papers .- Research on Image Segmentation Algorithm Based on Level Set. Ping Wu ((AVIC Shenyang Aircraft Design & Research Institute).- Predicting learners' performance using MOOC clickstream.- A Fine-grained Verification Method for Blockchain Data Based on Merkle Path Sharding.- A Privacy Preserving Method for Trajectory Data Publishing Based on Geo-indistinguishability.- HA-CMNet: A Driver CTR Model for Vehicle-Cargo Matching in O2O Platform.- A Hybrid Intelligent Model SFAHP-ANFIS-PSO for Technical Capability Evaluation of Manufacturing Enterprises.- A method for data exchange and management in the military industry field. Ping Wu ((AVIC Shenyang Aircraft Design & Research Institute).- Multi-region Quality Assessment based on Spatial-Temporal Community Detection from Computed Tomography Images.